Deep incremental learning-based intrusion detection in IoT environments is an emerging research area focused on developing adaptive and scalable security solutions for dynamic, heterogeneous IoT networks. Research papers in this domain explore the use of deep incremental learning models that can continuously update and refine their knowledge to detect new or evolving attacks without requiring complete retraining on historical data. Key contributions include lightweight and energy-efficient algorithms suitable for resource-constrained IoT devices, edge/fog-assisted real-time intrusion detection, and hybrid frameworks combining incremental learning with deep neural networks, autoencoders, or recurrent architectures. Recent studies also address challenges such as concept drift, class imbalance, scalability, and adversarial attacks in large-scale IoT deployments. By leveraging deep incremental learning, research in this area aims to provide intelligent, adaptive, and resilient intrusion detection systems capable of evolving with the threat landscape in IoT ecosystems.